DoppelLab: Tools for exploring and harnessing multimodal sensor network data

Dublon, Gershon; Pardue, Laurel; Mayton, Brian; Swartz, Noah; Joliat, Nicholas; Hurst, Patrick; Paradiso, Joseph A.

doi:10.1109/icsens.2011.6126903

Cited by 13 publications

(11 citation statements)

References 10 publications

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“…DoppelLab -a platform to create representations of general sensor network data -has been used to visualise and interrogate temperature data in workplaces [14]. An initial deployment of the system revealed malfunctions in the HVAC system, which had previously gone unnoticed.…”

Section: Making Building Operation Visiblementioning

confidence: 99%

Understanding Energy Consumption at Work

Bedwell

Costanza

Jewell

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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Most work around technological interventions for energy conservation to date has focussed on changing individual behaviour. Hence, there is limited understanding of communal settings, such as office environments, as sites for intervention. Even when energy consumption in the workplace has been considered, the emphasis has typically been on the individual. To address this gap, we conducted a study of energy consumption and management in one workplace, based on a combination of workshops with a broad range of stakeholders, and quantitative data inspections. We report and discuss findings from this study, in light of prior literature, and we present a set of implications for design and further research. In particular, three themes, and associated intervention opportunities, emerged from our data: (1) energy wastage related to "errors"; (2) the role of company policies and the negotiation that surrounds their implementation; and (3) the bigger energy picture of procurement, construction and travel.

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Section: Making Building Operation Visiblementioning

confidence: 99%

Understanding Energy Consumption at Work

Bedwell

Costanza

Jewell

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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“…We have been continually archiving this data set since 2010 and integrating it with data from our building's HVAC system through a 3D sensor data visualizer and interactive browser produced by our research team called Doppellab [8].…”

Section: Summery and Discussionmentioning

confidence: 99%

Dense, low-power sensor network for three-dimensional thermal characterization of large-scale atria spaces

et al. 2012

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Abstract-We describe the design and implementation of a dense, low-power wireless sensor network for fine-grained threedimensional thermal characterization of a large open indoor space. To better understand the airflow dynamics and ensuing energy efficiency potential of this type of modern architectural design, we developed a sustainable wireless mesh network consisting of 50 sensors hung on an array of thin cables in a 210 m 2 , 14.2 m tall atrium for real-time temperature and humidity monitoring. The goal is to create compact wireless measurement sensor blocks for dense coverage in the building. We demonstrate the implementation through a preliminary analysis, which includes the evaluation of temperature distribution discrepancies with computer-simulated results and data taken during natural ventilation to illustrate the nontrivial, well-mixed temperatures observed during the studies.

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“…It is reasonable to assume that in the near future, multiple-sensor functionality will be included in the officeplan lighting; perhaps increasing the density of motion sensor coverage on the floor. How can this dense motion-sensor array The Responsive Environments group's "Doppellab" enables spatio-temporal browsing of live sensor data in a virtual 3D environment [9]. be utilized to minimize type II errors in the lighting control network?…”

Section: A Lightingmentioning

confidence: 99%

Random walk and lighting control

et al. 2013

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Abstract-We pose the problem of turning off a single luminaire (or group) as an optimal stopping problem. We present the stationary and first-passage analysis of motion data obtained using custom wireless nodes in an open office floor plan. These calculations allow us to estimate the state of the network and calculate the probability and expected number of steps to visit a state from any arbitrary state. We also investigate if there is any evidence of clustering amongst the nodes by studying the covariance of the dataset. The data indicate the existence of clustering within the lattice. In other words, the analysis of random walk prevents luminaires from accidentally shutting off and dimensionality reduction determines the correct zoning of lighting via the occupants' movements.

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DoppelLab: Tools for exploring and harnessing multimodal sensor network data

Cited by 13 publications

References 10 publications

Understanding Energy Consumption at Work

Understanding Energy Consumption at Work

Dense, low-power sensor network for three-dimensional thermal characterization of large-scale atria spaces

Random walk and lighting control

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